Abstract
Nowadays there is a growing research interest on the possibility of enriching small flying robots with autonomous sensing and online navigation capabilities. This will enable a large number of applications spanning from remote surveillance to logistics, smarter cities and emergency aid in hazardous environments. In this context, an emerging problem is to track unauthorized small unmanned aerial vehicles (UAVs) hiding behind buildings or concealing in large UAV networks. In contrast with current solutions mainly based on static and on-ground radars, this paper proposes the idea of a dynamic radar network of UAVs for real-time and high-accuracy tracking of malicious targets. To this end, we describe a solution for real-time navigation of UAVs to track a dynamic target using heterogeneously sensed information. Such information is shared by the UAVs with their neighbors via multi-hops, allowing tracking the target by a local Bayesian estimator running at each agent. Since not all the paths are equal in terms of information gathering point-of-view, the UAVs plan their own trajectory by minimizing the posterior covariance matrix of the target state under UAV kinematic and anti-collision constraints. Our results show how a dynamic network of radars attains better localization results compared to a fixed configuration and how the on-board sensor technology impacts the accuracy in tracking a target with different radar cross sections, especially in non line-of-sight (NLOS) situations.
Highlights
The use of unmanned aerial vehicles (UAVs) in densely inhabited areas like cities is expected to open an unimaginable set of new applications thanks to their low-cost and high flexibility for deployment
Differently from the literature and from our previous works [19], [20], where usually radar sensor networks and UAVs are treated separately, this paper aims at introducing the concept of a monostatic dynamic radar network (DRN) consisting of UAVs carrying scanning radars of small sizes and weights, able to track a target and, simultaneously, adapt their formation-navigation control based on the quality of the signals backscattered by a non-cooperative flying target present in the environment
Each UAV can process the collected measurements in different ways: We indicate with Nr the set of UAVs that exclusively acquire ranging estimates, with Nd the group that only works with Doppler shifts, with Nb the set operating on bearing-only data, and with Nj the set that has access to all the types of measurements
Summary
The use of UAVs in densely inhabited areas like cities is expected to open an unimaginable set of new applications thanks to their low-cost and high flexibility for deployment. These approaches usually do not account for the dynamics of the environment (they define the entire paths a priori), and they do not consider NLOS biases or the effect of the target’s RCS in the measurement model They are not suitable for our scenario where the UAVs have to adjust their trajectories in real-time and in accordance to the movements of the unauthorized flying target that can be of small dimension (mini/micro UAV). Given this background, the aim of this paper is to study a UAV DRN as a cooperative radar sensing network for jointly tracking a non-authorized UAV in real-time and with high-accuracy and for smartly navigating the environment in order to reduce the correspondent tracking error (via multihop exchange of information). In×m and 0n×m indicate the identity and zero matrices of n × m size, respectively
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